Hierarchical data exchange management system

ABSTRACT

According to some embodiments, a system to facilitate hierarchical data exchange may include an aggregation platform data store containing electronic records. A data aggregation platform may collect, from a plurality of data source devices, information associated with a plurality of data sources and store the collected information into the aggregation platform data store. The data aggregation platform may also receive a data request from a data consumer device, and, responsive to the received data request, determine a precision tier associated with the data request. The data aggregation platform may then automatically calculate a resource value for the data request based on the precision tier. It may then be arranged for information from the aggregation platform data store to be modified and transmitted to the data consumer device.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.17/167,854, entitled “HIERARCHICAL DATA EXCHANGE MANAGEMENT SYSTEM,”filed on Feb. 4, 2021, now U.S. Pat. No. 11,323,544 which issued on May5, 2022, which is a continuation of U.S. patent application Ser. No.15/812,003, entitled “HIERARCHICAL DATA EXCHANGE MANAGEMENT SYSTEM”,filed Nov. 14, 2017, now U.S. Pat. No. 10,938,950 which issued on Mar.2, 2021, both of which are herein incorporated by reference in theirentireties.

BACKGROUND

Some embodiments disclosed herein relate to a data management systemand, more particularly, to systems and methods implementing or using ahierarchical data exchange management system.

One or more data consumers may be interested in obtaining informationfrom data sources. For example, people wearing fitness activity monitorsmay generate medical information, such as an hourly heart rate, thatmight be of interest to researchers. Moreover, different people may havedifferent preferences and/or willingness to share this type ofinformation. Further note that some types of information may be morevaluable to data consumers as compared to other types of information.For example, knowing that a person has a particular heart conditionmight be of interest to a researcher. In general, people may be willingto share more specific and/or more personal information in exchange forhigher levels of compensation. It can be difficult, however, to fairlyand accurately arrange for different parties to provide and/or receivedifferent types of information in exchange for different types ofbenefits—especially with a substantial number of people and/or a largenumber of transactions (e.g., tens of thousands of transactions), andthe process can be both time consuming and costly. It may therefore bedesirable to achieve improved and computerized ways to efficiently andaccurately facilitate management of a hierarchical data exchange.

SUMMARY

According to some embodiments, a system to facilitate hierarchical dataexchange may include an aggregation platform data store containingelectronic records. A data aggregation platform may collect, from aplurality of data source devices, information associated with aplurality of data sources and store the collected information into theaggregation platform data store. The data aggregation platform may alsoreceive a data request from a data consumer device, and, responsive tothe received data request, determine a precision tier associated withthe data request. The data aggregation platform may then automaticallycalculate a resource value for the data request based on the precisiontier. It may then be arranged for information from the aggregationplatform data store to be modified and transmitted to the data consumerdevice.

Some embodiments comprise: means for collecting, from a plurality ofdata source devices, information associated with a plurality of datasources; means for storing, at an aggregation platform data store,electronic records representing the collected information; means forreceiving, at a data aggregation computer processor, a data request froma data consumer device; responsive to the received data request, meansfor determining a precision tier associated with the data request;responsive to the received data request, means for determining a privacytier associated with the data request; means for automaticallycalculating a resource value for the data request based on the precisiontier and the privacy tier; means for arranging for information from theaggregation platform data store to be modified and transmitted to thedata consumer device; means for arranging for at least a portion of theresource value to be provided to at least one data source; and means forrecording information associated with the data request via a secure,distributed transaction ledger.

Technical effects of some embodiments of the invention are improved andcomputerized ways to efficiently and accurately facilitate management ofa hierarchical data exchange. With these and other advantages andfeatures that will become hereinafter apparent, a more completeunderstanding of the nature of the invention can be obtained byreferring to the following detailed description and to the drawingsappended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level diagram of a system according to someembodiments.

FIG. 2 is a method in accordance with some embodiments.

FIGS. 3A and 3B are examples of hierarchical data monetization inaccordance with some embodiments.

FIG. 4 is a more detailed view of a system according to someembodiments.

FIG. 5 illustrates a platform according to some embodiments.

FIG. 6 is a portion of a precision tier database in accordance with someembodiments.

FIG. 7 is a portion of a privacy tier database according to someembodiments.

FIG. 8 is a portion of a resource values database in accordance withsome embodiments.

FIG. 9 illustrates an interactive user interface display according tosome embodiments.

FIG. 10 is a system implementing hierarchical data monetizationtransactions with blockchain validation according to some embodiments.

FIG. 11 is a system implementing hierarchical data monetizationtransactions with multiple data aggregation platforms in accordance withsome embodiments.

FIG. 12 is a data supply chain for data markets according to someembodiments.

FIG. 13 is a distributed ledger reference architecture according to someembodiments.

FIG. 14 illustrates a tablet computer providing a display according tosome embodiments.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of embodiments.However, it will be understood by those of ordinary skill in the artthat the embodiments may be practiced without these specific details. Inother instances, well-known methods, procedures, components and circuitshave not been described in detail so as not to obscure the embodiments.

One or more specific embodiments of the present invention will bedescribed below. In an effort to provide a concise description of theseembodiments, all features of an actual implementation may not bedescribed in the specification. It should be appreciated that in thedevelopment of any such actual implementation, as in any engineering ordesign project, numerous implementation-specific decisions must be madeto achieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

It may therefore be desirable to achieve improved and computerized waysto efficiently and accurately facilitate a hierarchical data exchange.For example, FIG. 1 is a high-level diagram of a system 100 according tosome embodiments. The system 100 includes an automated data aggregationplatform 150 that communicates with one or more data sources 110 and oneor more data consumers 160. By way of example only, the data sources 110might comprise consumers who wear health monitoring devices and the dataconsumers 160 might comprise devices associated with medical researchersor insurance companies who are interested in the data generated by thehealth monitoring devices. According to some embodiments, the automateddata aggregation platform 150 can access an aggregation platform datastore 120 that includes electronic records reflecting informationprovided by the data sources 110. Note that the automated dataaggregation platform 150 could be completely de-centralized and/or mightbe associated with a third party, such as a vendor that performs aservice for an enterprise. Also note that although the data aggregationplatform data store 120 is illustrated in FIG. 1 , any of theembodiments described herein might be configured such that data sources110 instead transmit information directly to data consumers 160.

The automated data aggregation platform 150 and/or other elements of thesystem 100 might be, for example, associated with a Personal Computer(“PC”), laptop computer, a tablet computer, a smartphone, an enterpriseserver, a server farm, and/or a database or similar storage devices.According to some embodiments, an “automated” data aggregation platform150 may automatically manage a hierarchical data exchange. As usedherein, the term “automated” may refer to, for example, actions that canbe performed with little (or no) intervention by a human.

As used herein, devices, including those associated with the automateddata aggregation platform 150 and any other device described herein, mayexchange information via any communication network which may be one ormore of a Local Area Network (“LAN”), a Metropolitan Area Network(“MAN”), a Wide Area Network (“WAN”), a proprietary network, a PublicSwitched Telephone Network (“PSTN”), a Wireless Application Protocol(“WAP”) network, a Bluetooth network, a wireless LAN network, and/or anInternet Protocol (“IP”) network such as the Internet, an intranet, oran extranet. Note that any devices described herein may communicate viaone or more such communication networks.

The automated data aggregation platform 150 may store information intoand/or retrieve information from data stores, including the aggregationplatform data store 120. The data stores might, for example, storeelectronic records representing consumer health data, demographicinformation, etc. The data stores may be locally stored or reside remotefrom the automated data aggregation platform 150. Although a singleautomated data aggregation platform 150 is shown in FIG. 1 , any numberof such devices may be included. Moreover, various devices describedherein might be combined according to embodiments of the presentinvention. For example, in some embodiments, the automated dataaggregation platform 150, aggregation platform data store 120, and/orother devices might be co-located and/or may comprise a singleapparatus.

According to some embodiments, the data aggregation platform 150 mayarrange for information from data sources 110 to be stored in theaggregation platform data store 120. The data aggregation platform 150may then receive a data request from a data consumer 160. In accordancewith some embodiments, the data aggregation platform 150 may accessprecision tiers 152 and resource values 156 when responding to therequest. For example, a data consumer 160 might arrange to provide ahigher value (e.g., a higher benefit or a higher monetary value or otherstore of value) to the data aggregation platform 1500 in exchange foraccess to more precise information about the data sources 110 ascompared to less precise information. The data aggregation platform 150may then modify information in the aggregation platform data store 120(e.g., by filtering data, taking average values, etc.) and provide themodified information to the data consumer 160.

In this way, the system 100 may efficiently and accurately facilitatemanagement of a hierarchical data exchange. Note that the system 100 ofFIG. 1 is provided only as an example, and embodiments may be associatedwith additional elements or components. For example, FIG. 2 illustratesa method 200 that might be performed according to some embodiments ofthe present invention. The flow charts described herein do not imply afixed order to the steps, and embodiments of the present invention maybe practiced in any order that is practicable. Note that any of themethods described herein may be performed by hardware, software, or anycombination of these approaches. For example, a computer-readablestorage medium may store thereon instructions that when executed by amachine result in performance according to any of the embodimentsdescribed herein.

At 210, the system may collect, from a plurality of data source devices,information associated with a plurality of data sources. As used herein,the phrase “data source” might refer to an individual, a family, anenterprise, a business, or any other entity capable of providing data.At 220, the system may store, at an aggregation platform data store,electronic records representing the collected information. By way ofexample, the collected information might be associated with health datasuch as heart rate data, activity data, sleep data, blood pressure data,glucose monitoring data, insulin data, etc.

Note that embodiments do not need to be associated with health data. Forexample, the information collected from data sources (e.g., individuals)might include media consumption data such as television data (e.g.,which channels or programs an individual watches), online data (e.g.,what web sites does he or she visit), application data (e.g., whichsmartphone apps or video games does an individual access), streamingdata (e.g., what movies or television shows does he or she watch),advertising data, etc. The collected information could also includecommunication data such as telephone communication data (e.g., who doesthe person call and how often), email communication data, social networkcommunication data, real world proximity data (e.g., what people orgroups does he or she spend time interacting with in the real world),etc. As still other examples, the collected information might includedemographic data (e.g., age, gender, home address, etc.), psychographicdata (e.g., hobbies, mood, income, etc.), location data, telematic dataassociated with driving habits, survey data, genetic data, credit scoredata, spending data, credit card data, bank account data, etc. When thedata sources are businesses or similar entities, the collectedinformation might include sales data, profit data, employee data, debtdata, etc. According to some embodiments, data collected in connectionwith a business might include information about an industrial asset item(e.g., a wind turbine, a gas turbine, etc.), a “digital twin” thatmodels operation of a physical industrial asset item, an additivemanufacturing process, etc.

At 230, a data aggregation computer processor may receive a data requestfrom a data consumer device. As used herein, the phrase “data consumer”might refer to an enterprise, a business, an individual, or any otherentity interested in receiving data generated by data sources. Examplesof data consumers might include a researcher, an insurer, an advertiser,a governmental entity, an educational entity, etc. Note that a dataaggregation platform might be implemented via a single networkcloud-hosted topology, a multiple network cloud-hosted topology, aparticipant hosted intranet environment, etc.

At 240, responsive to the received data request, the system maydetermine a “precision tier” associated with the data request. As usedherein, the phrase “precision tier” may refer to various levels ofprecision associated with the data. For example, some types ofgranularity associated with precision tiers might be associated with acomplete data set (e.g., a person's heart rate as measured once per hourby an activity tracking device), an average of multiple data itemsassociated with a data source (e.g., a person's average heart rateduring a particular week), an average of multiple data items associatedwith multiple data sources (e.g., the average heart rate of all womenbetween the ages of 40 and 45), data items sharing at least onecharacteristic specified in the data request (e.g., the average heartrate of all people who have pacemakers), etc.

At 250, responsive to the received data request, the system maydetermine a “privacy tier” associated with the data request. As usedherein, the phrase “privacy tier” may refer to various levels ofspecificity associated with identifying a particular person or entity asbeing the data source. Examples of this type of information include apersonal identifier (e.g., a Social Security Number (“SSN”)), a name, ahealth condition, an age band (e.g., from 25 to 35 years old), abirthday, a location, an address (e.g., a home address or acommunication address such as an email), a gender, etc. In some cases,the privacy tier might be associated with complete anonymity (i.e., nopersonal data may be provided at all). Note that not all stepsillustrated in FIG. 2 might be performed in accordance with someembodiments of the present invention (e.g., as illustrated by dashedlines around some steps).

At 260, the system may then automatically calculate a “resource value”for the data request based on the precision tier and, in embodimentsthat have a privacy tier, the privacy tier. As used herein, the phrase“resource value” might refer to any type of benefit that is provided todata sources in exchange for sharing information. Note that data sourcesmay receive higher compensation in exchange for sharing more specificand/or more private data. For example, if a person's hourly heart ratewas transmitted to a researcher along with his or her name the amount ofcompensation might be much greater as compared to having that sameinformation being used to determine an average heart rate for all25-year-old men (in which case, all 25-year-old men might each receive amuch smaller amount of compensation). Examples of different types ofresource values include an online payment, a micropayment, a creditaccount payment, a debit account payment, a bank transfer, acryptocurrency and digital payment system, etc. According to someembodiments, a non-monetary benefit might be provided to a data source,such as access to data (e.g., the ability to watch a movie) or an amountof points to be subsequently redeemed by the data source (e.g., frequentflier miles).

At 270, the system may arrange for information from the aggregationplatform data store to be modified and transmitted to the data consumerdevice. The types of modifications that might be performed on theinformation from the aggregation platform data store include dataaggregation, averaging multiple data items associated with a single datasource, averaging multiple data items associated with multiple datasources, combining information from multiple data source devices eachassociated with a single data source, removing information (e.g.,de-personalization), supplementing information with third-party data(e.g., appending a person's credit score to a data file), datatranslation (e.g., from one format or protocol to another), etc. At 280,the system may then arrange for least a portion of the resource value tobe provided to at least one data source. That is, the data source may becompensated in exchange for having the data consumer receive his or herinformation via the data aggregation platform.

At 290, the system may record information associated with the datarequest via a secure, distributed transaction ledger. For example,details about the transaction may be recorded in a transaction ledgerassociated with blockchain technology. The recorded information mightinclude for example, data request information, data source information,payment information, data integrity information, precision information,privacy information, resource value information, indications of dataavailability, etc.

In this way, a hierarchy of information might be made available and/ormonetized by a data aggregation platform. For example, FIG. 3Aillustrates 300 hierarchical data monetization in accordance with someembodiments. In particular, a cloud platform 310 (e.g., associated witha data aggregation platform or website aggregator) might be able toaccess a substantial amount of information (e.g., including statisticaldata) associated with various people 320. For each person 320, varioustypes of detailed information might be available. As illustrated in FIG.3A, raw heart data 332 and activity data 334 might be available forperson 1 and person 2. Note that different people 320 might beassociated with different levels or types of data (e.g., either becauseof personal preference or the use of different data collection devices).For example, person 3 might have decided to make his or her locationdata 336 available in addition to the raw heart data 332 and activitydata 334. As another example, FIG. 3B illustrates 350 hierarchical datamonetization in accordance with some embodiments. In particular, a cloudplatform 360 (e.g., associated with a website aggregator) might be ableto access a substantial amount of information (e.g., includingstatistical data) associated with various gas or wind turbines 370. Foreach turbine 370, various types of detailed information might beavailable. As illustrated in FIG. 3B, kilo-Watt-hours (kWh) output 382and turbine speed 384 might be available for turbine 1 and turbine 2.Note that different turbines 370 might be associated with differentlevels or types of data (e.g., either because of the preference or abusiness operating the turbines 370 or the use of different sensornodes). For example, turbine 3 might have temperature data 386 availablein addition to the kWh output 382 and turbine speed data 384.

FIG. 4 is a more detailed view of a system 400 according to someembodiments. As before, the system 400 includes an automated dataaggregation platform 450 that communicates with one or more data sources410 (e.g., data sources 1 through n) and one or more data consumers 460.By way of example only, the data sources 410 might comprise consumerswho wear health monitoring devices and the data consumers 460 mightcomprise devices associated with medical researchers or insurancecompanies who are interested in the data generated by the healthmonitoring devices. At (A), the automated data aggregation platform 450arranges for information from the data sources 410 to be stored into anaggregation platform data store 420.

At (B), the data aggregation platform 450 receives a data request from adata consumer 460. In accordance with some embodiments, the dataaggregation platform 450 may access precision tiers 452, privacy tier454, and resource values 456 when responding to the request. Forexample, a data consumer 460 might arrange to provide a higher value tothe data aggregation platform 450 in exchange for access to more preciseinformation about the data sources 410 (along with personal information)as compared to less precise (and less personal) information. The dataaggregation platform 450 may then modify information in the aggregationplatform data store 420 (e.g., by filtering data, taking average values,etc.) and provide the modified information to the data consumer 460 at(C).

According to some embodiments, information from a third-party platform470 might be used to supplement or modify the information before it isprovided to the data consumer 460. For example, the third-party platform470 might add information about a person's income to records in theaggregation platform data store 420. After the information is providedto the data consumer 460, the data aggregation platform 450 mightutilize a payment platform 480 (e.g., a credit card or bankingapplication) to arrange for the data consumer 460 to provide paymentand/or for one or more data sources 410 to receive payment in exchangefor sharing information. Moreover, information about the transactionmight be recorded in a secure, distributed ledger (e.g., via blockchaintechnology). For example, information about the transaction that mightbe recording in a secure, distribute ledger includes information aboutthe data request from the data consumer, optionally modified by aprecision tier and/or privacy tier, payment information, data integrityinformation, etc.

Thus, the data aggregation platform 450 may be associated with data thatcan be described with different levels of fidelity and/or abstraction. Adata source 410 may choose to sell high fidelity data—for example, theirheart rate at high sample rate. From this data, it might be discerniblethat the person has an irregular heart rate—which an insurer could useto classify them as a “high risk” individual. As a result, thishigh-fidelity data might be very valuable to the insurer. Another dataconsumer 460 may not be interested in the high sample rate data, butwould instead be interested in average heart rates of groups ofpeople—for example, to determine a general level of health. Those datasources 410 contributing data at this level of fidelity can also becompensated, but perhaps at a reduced rate as compared to those whocontribute higher fidelity data. Note that the data aggregation platform450 and/or distributed ledger 490 may allow for the provenance,integrity, and/or confidentiality of hierarchical data using blockchaintechnology).

The data aggregation platform 450 may provide a means for a data source410 to be remunerated for increasingly detailed information—with higherfidelity (and more private) information being assessed at a higher valuethan lower granularity (e.g., averages or aggregated sets of data.According to some embodiment, the distributed ledger 490 may be used to:

-   -   facilitate payment transactions between data sources 410 and        data consumers 460;    -   publish availability of data and/or associated options for        granularity and data quality;    -   establish a tiered pricing model for data;    -   control access to data at agreed upon granularity;    -   establish authenticity and/or provenance of data; and    -   federate data and link to the aggregation platform data store        420.

In this way, embodiments described herein may comprise a tool thatfacilitates hierarchical data monetization and may be implemented usingany number of different hardware configurations. For example, FIG. 5illustrates a platform 500 that may be, for example, associated with thesystems 100, 400 of FIGS. 1 and 4 , respectively (as well as othersystems described herein). The platform 500 comprises a processor 510,such as one or more commercially available Central Processing Units(“CPUs”) in the form of one-chip microprocessors, coupled to acommunication device 520 configured to communicate via a communicationnetwork (not shown in FIG. 5 ). The communication device 520 may be usedto communicate, for example, with one or more remote data sources and/ordata consumers. Note that communications exchanged via the communicationdevice 520 may utilize security features, such as those between a publicinternet user and an internal network of an insurance enterprise. Thesecurity features might be associated with, for example, web servers,firewalls, and/or PCI infrastructure. The platform 500 further includesan input device 540 (e.g., a mouse and/or keyboard to enter informationabout a data source, a data hierarchy, pricing information, etc.) and anoutput device 550 (e.g., to output system reports, generate datamonetization dashboards, etc.).

The processor 510 also communicates with a storage device 530. Thestorage device 530 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, mobile telephones, and/orsemiconductor memory devices. The storage device 530 stores a program512 and/or network security service tool or application for controllingthe processor 510. The processor 510 performs instructions of theprogram 512, and thereby operates in accordance with any of theembodiments described herein. For example, the processor 510 mayfacilitate hierarchical data exchange by collecting, from a plurality ofdata source devices, information associated with a plurality of datasources. The processor 510 may also receive a data request from a dataconsumer device, and, responsive to the received data request, determinea precision tier associated with the data request. The processor 510 maythen automatically calculate a resource value for the data request basedon the precision tier. It may then be arranged by the processor 510 forinformation from the aggregation platform data store to be modified andtransmitted to the data consumer device.

The program 512 may be stored in a compressed, uncompiled and/orencrypted format. The program 512 may furthermore include other programelements, such as an operating system, a database management system,and/or device drivers used by the processor 510 to interface withperipheral devices.

As used herein, information may be “received” by or “transmitted” to,for example: (i) the platform 500 from another device; or (ii) asoftware application or module within the platform 500 from anothersoftware application, module, or any other source.

In some embodiments (such as shown in FIG. 5 ), the storage device 530further stores a precision tier database 600, privacy tier database 700,and resource values database 800. Examples of databases that might beused in connection with the platform 500 will now be described in detailwith respect to FIGS. 6 through 8 . Note that the databases describedherein are only examples, and additional and/or different informationmay be stored therein. Moreover, various databases might be split orcombined in accordance with any of the embodiments described herein. Forexample, precision tier database 600 and the privacy tier database 700might be combined and/or linked to each other within the program 512.

Referring to FIG. 6 , a table is shown that represents the precisiontier database 600 that may be stored at the platform 500 in accordancewith some embodiments. The table may include, for example, entriesidentifying different levels of data granularity or specificity. Thetable may also define fields 602, 604, 606 for each of the entries. Thefields 602, 604, 606 may, according to some embodiments, specify: aprecision tier identifier 602, precision tier description 604, andresource value 606. The precision tier database 600 may be created andupdated, for example, based on information electrically received fromdata sources, a data aggregation platform administrator, etc.

The precision tier identifier 602 may be, for example, a uniquealphanumeric code identifying a level of data granularity orspecificity. The precision tier description 604 may described the levelof data granularity or specificity associated with data in that tier(e.g., from the most specific “hourly heart rate” to the least specific“overall lifetime average heart rate”). The resource value 606 mightrepresent, for example, a monetary value or some other benefit thatmight be provided by a data consumer and/or provided to a data source.Note that more specific data might be associated with higher resourcevalues 606.

Referring to FIG. 7 , a table is shown that represents the privacy tierdatabase 700 that may be stored at the platform 500 in accordance withsome embodiments. The table may include, for example, entriesidentifying levels of personal information associated with data. Thetable may also define fields 702, 704, 706 for each of the entries. Thefields 702, 704, 706 may, according to some embodiments, specify: aprivacy tier identifier 702, privacy tier description 704, and aresource value 706. The privacy tier database 700 may be created andupdated, for example, based on information electrically received fromdata sources, a data aggregation platform administrator, etc.

The privacy tier identifier 702 may be, for example, a uniquealphanumeric code identifying a level of personal information and may bebased on or associated with the privacy tier identifier 802 in theresource values database 800. The privacy tier description 704 maydescribe the level of personal information associated with data in thattier (e.g., from the most personal “exact name/SSN” to the leastpersonal “no information”). The resource value 706 might represent, forexample, a monetary value or some other benefit that might be providedby a data consumer and/or provided to a data source. Note that morepersonal data might be associated with higher resource values 706.

Referring to FIG. 8 , a table is shown that represents the resourcevalues database 800 that may be stored at the platform 500 in accordancewith some embodiments. The table may include, for example, entriesidentifying different resource values that are assigned to variouslevels of precision and/or privacy. The table may also define fields802, 804, 806 for each of the entries. The fields 802, 804, 806 may,according to some embodiments, specify: a privacy tier identifier 802, aprivacy tier description 804, and resource values 806 for variousprecision levels. The resource values database 800 may be created andupdated, for example, based on information electrically received fromdata sources, data providers, a data aggregation platform administrator,etc.

The privacy tier identifier 802 may be, for example, a uniquealphanumeric code identifying a level of personal information and may bebased on or associated with the privacy tier identifier in the privacytier database 700. The privacy tier description 804 may describe thelevel of personal information associated with data in that tier (e.g.,including a lower level of personal information “age band or birthday”and a relatively higher level of personal information “gender”). Theresource values 808 might be specified for each of a number of differentlevels of data precision. That is, the resource values 808 mightrepresent a matrix of benefit that might be provided with more preciseand/or more personal data being associated with higher benefit ascompared to less precise and/or less personal data.

The information in the precision tier database 600, privacy tierdatabase 700, and/or resource values database 800 might be monitoredand/or updated by a data aggregation platform administrator. Forexample, FIG. 9 illustrates an interactive user interface display 900according to some embodiments. The display 900 includes a data hierarchygraphical user interface 910 including a cloud platform (e.g., a websiteaggregator), statistical data for various people, and specific types ofdata elements. According to some embodiments, selection of an element inthe interface 910 (e.g., via a computer mouse pointer 920 or touchscreen) results in further information about that element beingdisplayed (e.g., an associated resource value might be displayed in apop-up window and/or adjusted by an administrator). Selection of an“Export Data” icon might, according to some embodiments, result in atransfer of data from a data source to a data consumer.

A data aggregation platform and/or other elements of a data hierarchymonetization system may record information about transactions using asecure, distributed transaction ledger (e.g., via a blockchainverification process). For example, the data aggregation platform mightrecord a request date and time, a data description, a data sourceidentifier, a price, a bid, etc. via the secure, distributed transactionledger in accordance with any of the embodiments described herein.According to some embodiments, the distributed ledger might beassociated with the HYPERLEDGER® blockchain verification system. FIG. 10is a system 1000 implementing hierarchical data monetizationtransactions incorporating blockchain validation according to someembodiments. A cloud-based integrity monitor 1010 may providetransaction integrity data via a web browser and exchange informationwith a blockchain 1020 (or other secure distributed transaction ledger)and a data aggregation platform 1050 via Representational State Transfer(“REST”) web services or other similar web services. The REST webservices may, for example, provide interoperability between computersystems on the Internet (e.g., by allowing requesting systems to accessand manipulate textual representations of web resources using a uniform,predefined set of stateless operations). According to some embodiments,portions of the data aggregation platform 1050 may be associated withdatabase, such as a MySQL database. In this way, the data aggregationplatform 1050 and blockchain 1020 can be used to provide transactionlevel verification for a client 1040 (including, for example,information about one or more hierarchical data transactions). AlthoughFIG. 10 illustrates a system 1000 with a single blockchain 1020 and dataaggregation platform 1050, note that embodiments may employ othertopologies. For example, FIG. 11 is a system 1100 implementing ahierarchical data monetization transaction incorporating multiple dataaggregation platforms 1150, 1152 in accordance with some embodiments. Inparticular, an additional blockchain 1122 and data aggregation platform1152 may provide protection for an additional client 1142. Asillustrated in FIG. 11 , each data aggregation platform 1150, 1152 maybe associated with multiple blockchains 1120, 1122 providing additionalprotection for the system 1100 (e.g., by storing information atmultiple, geographically disperse nodes making attacks impractical).That is, each verifier (e.g., data aggregation platform 1150, 1152) maycommit a brief summary to an independent data store (including forexample, information about hierarchical data transaction) and, oncerecorded, the information cannot be changed without detection to providea tamper-proof System of Records (“SoR”).

FIG. 12 is a data supply chain 1200 for data markets according to someembodiments. Information from various data sources 1210 (data source 1through n) may be aggregated and/or normalized via analytics 1220.Application of insight analytics 1230 may result in actionable analytics1240 that can be implemented via actuation and control processes 1250(e.g., including both digital and physical implementations). Forexample, one or more components of the data supply chain 1200 mightrequest information that has been generated by the data sources 1210(e.g., the component that looks for actionable analytics 1240 might actas a data consumer who requests data associated with specific precisionand pricing levels). Such transactions may be recorded via a transactionand authentication service 1260 (e.g., utilizing blockchain) and/orretained in a storage service 1270. Note that transactions providinginformation from a data source 1210 to a data consumer might beimplemented in a number of different ways, including, for example: a peruse or limited use license (e.g., of data generated by one or more datasources 1210), a sell-out license, a sub-license right, etc. Moreover,embodiments might utilize supply chain factoring, incremental upgrades,and/or anonymized data and transactions. Information may be traceableand/or auditable back to an original source (e.g., data source 1210)and, in some embodiments, be tagged through an entire chain, tree, ormesh associated with the transaction. Automated rules and/or processes,including a pre-programmed bot, might be used to negotiate pricesbetween data sources 1210 and data consumers (e.g., prices associatedwith various precision and privacy levels). In some cases, parameterizedand/or machine learning might facilitate such negotiations. Note thatdata values (e.g., ranging from a free give away to substantialmonetization) might be based on various factors including datacompression, distance from a data source 1210, copyright as a service,etc.

The information associated with transactions in the supply chain 1200might, for example, represent three-dimensional printing files (e.g.,for additive manufacture), optical displays, audio streams, etc.Moreover, various components of the supply chain 120 could provideadditional services, such as certification, authentication (withblockchain being only one option among many), license rights services,quality control, use control and restrictions, anti-counterfeitmeasures, etc.

The transaction and authentication service 1260 might, according to someembodiments, be associated with any type of distributed ledger having ade-centralized consensus-based network that supports smart contracts,digital assets, record repositories, and/or cryptographic security. Forexample, FIG. 13 is a distributed ledger reference architecture 1300according to some embodiments. The architecture 1300 includes ledgerservices and an event stream 1310 that may contain hierarchical datatransaction information (e.g., from a data aggregation platform).Membership services 1320 (e.g., including registration, identitymanagements, and/or an auditability process) may manage identity,privacy, and confidentiality for membership 1350 for the networksecurity service. Blockchain services (e.g., including a consensusmanager, Peer-to-Peer (“P2P”) protocol, a distributed ledger, and/orledger storage) may manage the distributed ledger, for example, througha P2P protocol to maintain a single state that replicated at many nodesto support blockchains 1360 and transactions 1370. Chaincode services1340 (e.g., secure container and/or a secure registry associated with asmart contract) may help compartmentalize smart contract (or chaincode1380) execution on validating nodes. Note that the environment may be a“locked down” and secured container with a set of signed base imagesthat contain a secure OS and programming languages. Finally, APIs,Software Development Kits (“SDKs”), and/or a Command Line Interface(“CLI”) may be utilized to support a network security service via thereference architecture 1300. The information recorded via thearchitecture 1300 might include, for example, data request information,data source information, payment information, data integrityinformation, precision information, privacy information, resource valueinformation, indications of data availability, etc.

Thus, some embodiments described herein may provide technical advantagesand help solve an “all or nothing” problem where data is shared in fullor not shared at all (which can limit a person's willingness to sharedata and also limit the development of data markets where differentprice points are required or desired for different levels ofdisclosure). Moreover, embodiments may democratize a data market anddis-intermediate a data aggregator that currently monopolize the marketplace. Through the creation of multi-tier pricing models, andtechnologies that manage access to increasingly higher fidelity data, apayment mechanism may be established that mutually benefits multipleparties—data buyers and sellers, data sources, data consumers, etc.

The following illustrates various additional embodiments of theinvention. These do not constitute a definition of all possibleembodiments, and those skilled in the art will understand that thepresent invention is applicable to many other embodiments. Further,although the following embodiments are briefly described for clarity,those skilled in the art will understand how to make any changes, ifnecessary, to the above-described apparatus and methods to accommodatethese and other embodiments and applications.

Although specific hardware and data configurations have been describedherein, note that any number of other configurations may be provided inaccordance with embodiments of the present invention (e.g., some of theinformation described herein may be combined or stored in externalsystems). Moreover, although embodiments have been described withrespect to specific types of data, note that embodiments might beassociated with other types of data, including streaming entertainment,three-dimensional models, etc. Similarly, the displays shown anddescribed herein are provided only as examples, and other types ofdisplays and display devices may support any of the embodiments. Forexample, FIG. 14 illustrates a tablet computer 1400 with an interactiveresource value display 1410 that might utilize a graphical userinterface. The display 1410 might comprise matrix of prices associatedwith various levels of precision and/or privacy. Note that selection ofan element on the display 1410 might result in a display of furtherinformation about that element. Moreover, the display 1410 mightcomprise an interactive user interface (e.g., via a touchscreen) andinclude an “Adjust” 1420 icon to let an operator or administrate changevarious tiers and/or prices points in accordance with any of theembodiments described herein.

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

The invention claimed is:
 1. A system, comprising: a data aggregationplatform comprising: a communication port; and a data aggregationcomputer processor coupled to the communication port, wherein the dataaggregation computer processor is adapted to: access an aggregationplatform data store containing collected data items associated with aplurality of data sources; receive a data request from a data consumerdevice, and responsive to the received data request, determine aprecision tier associated with the data request, wherein the precisiontier is associated with a level of detail of the data items from theplurality of data sources, and wherein the precision tier is associatedwith a plurality of data items collected over a period of time.
 2. Thesystem of claim 1, wherein the data aggregation computer processor isadapted to record information associated with the data request via asecure, distributed transaction ledger comprising blockchain technology.3. The system of claim 1, wherein the data aggregation computerprocessor is adapted to automatically calculate a resource value for thedata request based on the precision tier.
 4. The system of claim 3,wherein the data aggregation computer processor is adapted to determinea privacy tier associated with the data request and data related to anidentity of one of the plurality of data sources, wherein theautomatically calculated resource value is further based on the privacytier.
 5. The system of claim 3, wherein the resource value is associatedwith a benefit provided to the plurality of data sources in exchange forproviding the information.
 6. The system of claim 1, wherein theplurality of data sources comprises individuals.
 7. The system of claim1, wherein the collected data items are associated with demographicdata, psychographic data, location data, telematic data, survey data,genetic data, credit score data, spending data, credit card data, bankaccount data, health data, media consumption data, or communicationdata, wherein: health data includes at least one of heart rate data,activity data, sleep data, blood pressure data, glucose monitoring data,and insulin data; media consumption data includes at least one oftelevision data, online data, application data, streaming data,advertising data; and communication data includes at least one oftelephone communication data, email communication data, social networkcommunication data, and real world proximity data.
 8. The system ofclaim 1, wherein the data sources comprise business entities and thecollected data items are associated with at least one of: sales data,profit data, employee data, debt data, an industrial asset item, adigital twin, and an additive manufacturing process.
 9. The system ofclaim 1, wherein the level of detail of the precision tier is associatedwith at least one of: a data set comprising multiple data items of asingle type associated with one data source of the plurality of datasources, an average of multiple data items associated with multiple datasources of the plurality of data sources, and data items sharing atleast one characteristic specified in the data request.
 10. The systemof claim 1, wherein the data aggregation computer processor is adaptedto modify the collected data items by at least one of: data aggregation,averaging multiple data items associated with a single data source,averaging multiple data items associated with multiple data sources,combining data items from multiple data sources of the plurality of datasources, removing information, supplementing data items with third-partydata, and data translation.
 11. A system, comprising: a data aggregationplatform comprising: a communication port; and a data aggregationcomputer processor coupled to the communication port, wherein the dataaggregation computer processor is adapted to: access an aggregationplatform data store containing collected data items associated with aplurality of data sources; receive a data request from a data consumerdevice; responsive to the received data request, determine a precisiontier associated with the data request, wherein the precision tier isassociated with a level of detail of the data items from the pluralityof data sources; and calculate a resource value for the data requestbased on the precision tier.
 12. The system of claim 11, wherein thedata aggregation computer processor is adapted to determine a privacytier associated with the data request and data related to an identity ofone of the plurality of data sources.
 13. The system of claim 11,wherein the calculated resource value is further based on the privacytier.
 14. The system of claim 11, wherein the data aggregation computerprocessor is further adapted to arrange for at least a portion of theresource value to be provided to at least one data source.
 15. Thesystem of claim 11, wherein the resource value is associated with abenefit provided to the plurality of data sources in exchange forproviding the information.
 16. A computer-implemented method,comprising: receiving, at a data aggregation computer processor, a datarequest from a data consumer device; accessing, by the data aggregationcomputer processor, an aggregation platform data store containingcollected data items associated with a plurality of data sources;responsive to receiving the data request, determining, by the dataaggregation computer processor, a precision tier associated with thedata request, wherein the precision tier is associated with a level ofdetail of the information from the plurality of data sources; andcalculating, by the data aggregation computer processor, a resourcevalue for the data request based on the precision tier.
 17. Thecomputer-implemented method of claim 16, comprising recordinginformation associated with the data request via a secure, distributedtransaction ledger comprising blockchain technology.
 18. Thecomputer-implemented method of claim 17, comprising determining aprivacy tier associated with the data request and data related to anidentity of one of the plurality of data sources, and wherein thecalculated resource value is further based on the privacy tier.
 19. Thecomputer-implemented method of claim 18, wherein the collectedinformation is associated with health data and includes at least one of:heart rate data, activity data, sleep data, blood pressure data, glucosemonitoring data, and insulin data.
 20. The computer-implemented methodof claim 16, wherein the precision tier is associated with a pluralityof data items collected over a period of time.